To ensure future food security, a key objective of crop breeding programs is to
effectively identify which genetic and physiological characteristics of the
plant are associated with high yield and/or resistance to environmental
stressors. Regarding physiology, the part of the plant that is above-ground is
easily observed and thus commonly emphasized. However, the root system is
perceivably more sensitive to soil-related stressors yet notoriously challenging
to (a) measure and (b) characterize. For (a), recent imaging technology can
evaluate the number of roots at regular depths along soil cores that are sampled
from the crop field. This method results in 1-dimensional spatial data on
within-core root counts. For (b), we develop an integrative modelling framework
that regards the spatial count data as longitudinal in nature, exhibiting a
parametric trend that depends on the plant's genotype (or "breed"). Under our
framework, we define new measures of heritability — the variability among cores
that is due to genetics as opposed to noise. The novelty of our methodology lies
in the ability to reflect root architecture as a whole by accounting for
within-core root counts collectively (DOI: 10.3389/fpls.2017.00282). Applied to
a field study in Australia, our approach indicates an overall heritability of
0.52-0.71 (95% credible interval), which is substantially higher than previous
methods. This suggests that our approach is much more effective in discerning
root architecture as captured by soil core data.